Differentially Private computed data Generation for Artificial Intelligence Agriculture Yield Modeling Under Cooperative Survey Disclosure Rules: A Computer Utility-Loss Audit

Cooperatives that hold agricultural survey records face a disclosure problem: yield models trained on member-level data can leak the very information members supplied under confidentiality. Differential privacy offers a formal release guarantee, but its utility cost for the tabular survey indicators used in yield modelling is not well characterised. The audit formalises the computed-data release as a randomised mechanism, derives an (epsilon, delta)-differential privacy bound for the histogram-based generator, and defines a utility-loss functional that separates marginal distortion from conditional distortion. Using the supplied indicator as the audit substrate, the paper computes the sensitivity of the release, the privacy budget decomposition across the marginal and conditional release stages, and the utility-loss bounds that follow. The dominant utility loss arises not from the marginal histogram but from the conditional release used to preserve cross-tabulation structure, and that the loss grows with the number of quasi-identifier attributes rather than with sample size. The contribution is a reproducible audit protocol and a set of bounds that let cooperatives choose a privacy budget with an explicit statement of the modelling utility forgone.